Tracking of Multiple Targets Across Distributed Platforms with FOV Constraints
Bethany L. Allik · 2019
This paper considers the scenario of tracking multiple moving targets across spatially distributed sensors with limited dynamic range. The targets considered are indistinguishable based on signal return or appearance, therefore, utilizing all available information across operating sensors is paramount for reducing target distribution uncertainty. Previous work has used the FOV limitations to utilize absent detection's of non responding sensors to improve posterior probability estimation. For the measurement to target assignment problem, the Joint Probabilistic Data Association particle filter is used for tracking targets from several aerial platforms with nadir cameras. The estimator continuously updates target position states while obtaining direct measurement of the targets or non-observations. In addition to modeling dynamic range constraints, it is also assumed there is a known probability of detection which will be implemented in addition to the field of view limitations to derive the likelihood function for the particle filter. It is shown that non-observations of the target improves the posterior target distribution, lowering the uncertainty when the target cannot be directly observed. In addition to presenting a filter that performs data association and tracking, a track-health monitoring scheme is proposed that monitors system performance.